Navigation systems are available that provide end users with various navigation-related functions and features. For example, some navigation systems are able to determine an optimum route to travel along a road network from an origin location to a destination location in a geographic region. Using input from the end user, the navigation system can examine various potential routes between the origin and destination locations to determine the optimum route based on distance, travel time, road type, points of interest or other factors or combinations thereof. The navigation system may then provide the end user with information about the optimum route in the form of guidance that identifies the maneuvers required to be taken by the end user to travel from the origin to the destination location. Some navigation systems are able to show detailed maps on displays outlining the route, the types of maneuvers to be taken at various locations along the route, locations of certain types of features, and so on.
Some navigations systems may further provide real time traffic data/and or historical traffic data, such as via a map overlay showing such data in relation to the associated roads and/or by factoring such data into travel time or estimated arrival time calculations, e.g. via color codes, etc. Such data is typically provided by a Traffic company. Real time traffic data provides a snapshot of the current traffic conditions on the roads. Historical traffic data, also referred to as “traffic patterns,” provides expected speeds for any given time and day, not taking into account the current conditions. One such Traffic company is Here Global B.V. which provide the Navteq Traffic Patterns™ service. Traffic pattern data may reflect a composite value, such as an average, of the speed measured over a period of time, accounting for various known recurring, cyclical or permanent conditions, e.g. variations in time of day, day of week etc. The result is a service which provides a representation of the expected speed of a road for a variety of conditions.
Predicted traffic speeds may be useful for users, for example, to estimate travel times more accurately for short term future trips. For example, the predictive traffic speed output will help users make decisions like when to start a trip to airport for a flight departing in the next couple of hours. This data may be utilized by navigation systems, governmental or regulatory agencies, news organizations, and/or other service providers to present users with accurate representations of expected road conditions and/or to compute accurate predicted travel times to a destination via various mediums such as a navigation system display, television, radio, SMS, electronic road sign, etc. In one application, a public or private bus system may predict and publish, such as via electronic signage located at bus stops or a mobile phone app, estimated arrival times of the busses which stop there at. Trucking companies may predict deliveries, adjust schedules or routes, estimate costs, etc.
Generally, historical traffic pattern (“TP”) speed data is a composite of speed data measured over a period of time, and which may be partitioned based on previously known, recurring or permanent/fixed conditions such as time of day, day of week, scheduled occurrence of sporting or civic events, weather conditions, e.g. precipitation, no precipitation, etc., or combinations thereof, and may be further modeled or processed, such as statistically processed, normalized, etc., so as to provide an accurate estimate of the typical speed of a road at a given time and under a given occurrence of a recurring condition or event. It will be appreciated that modeling and/or statistical processing may be used to remove, or minimize, the effect of anomalous or outlier speed measurements which may skew the estimates. TP data may be centrally computed and accumulated and distributed, such as via a wireless network, on a subscription or other basis, such as via request or TP data for a particular road under particular conditions. Alternatively, or in addition thereto, TP data, or a portion thereof, such as for a given region, may be stored in medium, such as a volatile or non-volatile memory, e.g. an optical media, ROM or flash memory, and distributed to subscribers/purchasers to be used, for example, in conjunction with user's navigation system. Periodic updates to the TP data may then be distributed, via the same medium or via electronically distributed data updates, such as via a network.
Generally, real time RT data merely provides the current speed of the road measured or modeled at a particular time (or a composite, e.g. average, of measured values over a relatively short interval, e.g. five, ten, twenty or thirty minutes, etc.). As RT data is intended to represent the actual speed at the time of the measurement, the data may generally be provided in a substantially unprocessed form. For example, anomalous, temporarily aberrant and/or outlier RT speed measurements may or may not be retained, depending upon the implementation, so as to, for example, minimize their impact. RT data may be collected from the vehicle of the user using the disclosed embodiments, from other vehicles, e.g. probe vehicles, gps-enabled devices, e.g. smart phones, road sensors, traffic cameras, traffic reports, witnesses, etc. RT data may be centrally collected and distributed/broadcast, e.g. via a wireless network, to receivers/subscribers, such as mobile or portable navigation systems, news organizations, electronic road signs, etc. Alternatively, or in addition thereto, RT data may be collected by a mobile/portable navigation system or traffic reporting system for its own use. It will be appreciated that RT data collected by road sensors, probe vehicles, etc. may be distributed via wireless peer to peer or mesh based networks, e.g. the data is passed from a source and then from vehicle to vehicle, each navigation system within a vehicle being both a consumer of the data and a repeater thereof.
Existing traffic products use RT data (device id, latitude, longitude, speed, heading, sample time) from a variety of probe data sources (e.g. fleet vehicles and consumer devices) to create real-time depictions of traffic, e.g. to provide red-yellow-green status of road network segments based on the probe report for the road network segments. However, the penetration of probe data, i.e. the number of available and/or reliable data sources for a given road at a given time, varies geospatially and can be sparse. As a result, in the absence of real-time data, an algorithm must provide an estimate of the traffic dynamics.
Current approaches to building traffic models rely heavily on probe data sources. Rudimentary models for filling the estimate in the absence of data often are used. These rudimentary models may simply be fixed speed vs. time tables that are derived from historical data once a year. As such, these rudimentary models cannot accommodate changes in dynamics that are associated with common changes in traffic dynamics, such as holidays. Furthermore, these low level models for each segment operate independently and consequently cannot predict dynamics that are highly unlikely. For example, on a holiday these models will predict standard rush hour traffic when in fact the road network is in free flow.